updated app

app.py

@@ -1,7 +1,238 @@
 import gradio as gr
+import torch
+from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
+import torch
+import torchvision.transforms as T
+from PIL import Image
+import time
+import os, sys
+import json
+import re
+from tqdm import tqdm
 
-def greet(name):
-    return "Hello " + name + "!!"
+import pandas as pd
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+from torchvision.transforms.functional import InterpolationMode
+# Define the path to your model
+path = 'h2oai/h2o-mississippi-2b'
+
+# image preprocesing
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+start_pre = time.time()
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images, target_aspect_ratio
+
+
+def dynamic_preprocess2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    new_target_ratios = []
+    if prior_aspect_ratio is not None:
+        for i in target_ratios:
+            if prior_aspect_ratio[0]%i[0] != 0 and prior_aspect_ratio[1]%i[1] != 0:
+                new_target_ratios.append(i)
+            else:
+                continue
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, new_target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+def load_image1(image_file, input_size=448, min_num=1, max_num=12):
+    if isinstance(image_file, str):
+        image = Image.open(image_file).convert('RGB')
+    else:
+        image = image_file
+    transform = build_transform(input_size=input_size)
+    images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values, target_aspect_ratio
+
+def load_image2(image_file, input_size=448, min_num=1, max_num=12, target_aspect_ratio=None):
+    
+    if isinstance(image_file, str):
+        image = Image.open(image_file).convert('RGB')
+    else:
+        image = image_file
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess2(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num, prior_aspect_ratio=target_aspect_ratio)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+def load_image_msac(file_name):
+    pixel_values, target_aspect_ratio = load_image1(file_name, min_num=1, max_num=6)
+    pixel_values = pixel_values.to(torch.bfloat16).cuda()
+    pixel_values2 = load_image2(file_name, min_num=3, max_num=6, target_aspect_ratio=target_aspect_ratio)
+    pixel_values2 = pixel_values2.to(torch.bfloat16).cuda()
+    pixel_values = torch.cat([pixel_values2[:-1], pixel_values[:-1], pixel_values2[-1:]], 0)
+    return pixel_values
+# Load the model and tokenizer
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
+).eval().cuda()
+
+tokenizer = AutoTokenizer.from_pretrained(
+    path, 
+    trust_remote_code=True, 
+    use_fast=False
+)
+tokenizer.pad_token = tokenizer.unk_token
+tokenizer.eos_token = "<|end|>"
+model.generation_config.pad_token_id = tokenizer.pad_token_id
+
+
+def inference(image, prompt):
+    # Check if both image and prompt are provided
+    if image is None or prompt.strip() == "":
+        return "Please provide both an image and a prompt."
+    
+    # Process the image and get pixel_values
+    pixel_values = load_image_msac(image)
+
+    # Set generation config
+    generation_config = dict(
+        num_beams=1,
+        max_new_tokens=2048,
+        do_sample=False,
+    )
+
+    # Generate the response
+    response = model.chat(
+        tokenizer, 
+        pixel_values, 
+        prompt, 
+        generation_config
+    )
+
+    return response
+
+# Build the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("H2O-Mississippi")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload an Image")
+        prompt_input = gr.Textbox(label="Enter your prompt here")
+
+    response_output = gr.Textbox(label="Model Response")
+
+    with gr.Row():
+        submit_button = gr.Button("Submit")
+        clear_button = gr.Button("Clear")
+
+    # When the submit button is clicked, call the inference function
+    submit_button.click(
+        fn=inference, 
+        inputs=[image_input, prompt_input], 
+        outputs=response_output
+    )
+
+    # Define the clear button action
+    def clear_all():
+        return None, "", ""
+
+    clear_button.click(
+        fn=clear_all, 
+        inputs=None, 
+        outputs=[image_input, prompt_input, response_output]
+    )
+
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,9 @@
+fastapi
+opencv-python
+gradio==3.35.2
+gradio_client==0.2.9
+httpx==0.24.0
+markdown2[all]
+pydantic
+requests
+uvicorn